app.py

"""
BILLION DOLLAR EDUCATION AI - MULTIMODAL DATASET SUPREMACY
100% Free • Groq-Only • Dataset-Powered • Images + PDFs + Documents
The Ultimate Educational AI with File Processing Capabilities
"""

import gradio as gr
import requests
import json
import random
import threading
import time
import base64
import io
import os
from typing import Dict, List, Optional, Union
import asyncio
import aiohttp
from PIL import Image
import PyPDF2
import docx
import pandas as pd

# Safe dataset import
try:
    from datasets import load_dataset
    DATASETS_AVAILABLE = True
except ImportError:
    DATASETS_AVAILABLE = False
    def load_dataset(*args, **kwargs):
        return []

class MultimodalProcessor:
    """Handles images, PDFs, documents, and other file types"""
    
    def __init__(self):
        self.supported_formats = {
            'images': ['.png', '.jpg', '.jpeg', '.gif', '.bmp', '.webp'],
            'documents': ['.pdf', '.docx', '.doc', '.txt'],
            'data': ['.csv', '.xlsx', '.xls'],
            'code': ['.py', '.js', '.html', '.css', '.java', '.cpp', '.c']
        }
    
    def process_file(self, file_path: str) -> Dict[str, str]:
        """Process uploaded file and extract content/description"""
        if not file_path or not os.path.exists(file_path):
            return {"type": "error", "content": "File not found"}
        
        file_ext = os.path.splitext(file_path)[1].lower()
        
        try:
            if file_ext in self.supported_formats['images']:
                return self.process_image(file_path)
            elif file_ext in self.supported_formats['documents']:
                return self.process_document(file_path)
            elif file_ext in self.supported_formats['data']:
                return self.process_data_file(file_path)
            elif file_ext in self.supported_formats['code']:
                return self.process_code_file(file_path)
            else:
                return {"type": "unknown", "content": f"Unsupported file type: {file_ext}"}
        
        except Exception as e:
            return {"type": "error", "content": f"Error processing file: {str(e)}"}
    
    def process_image(self, image_path: str) -> Dict[str, str]:
        """Process image files - describe content for educational context"""
        try:
            with Image.open(image_path) as img:
                # Convert to base64 for potential API calls
                buffer = io.BytesIO()
                img.save(buffer, format='PNG')
                img_base64 = base64.b64encode(buffer.getvalue()).decode()
                
                # Basic image analysis
                width, height = img.size
                mode = img.mode
                format_type = img.format
                
                description = f"""IMAGE ANALYSIS:
- Dimensions: {width}x{height} pixels
- Format: {format_type}
- Color Mode: {mode}
- File Size: {os.path.getsize(image_path)} bytes

EDUCATIONAL CONTEXT:
This appears to be an image that may contain:
- Mathematical diagrams, graphs, or equations
- Scientific illustrations or charts  
- Educational content requiring visual analysis
- Homework problems or textbook materials

I can help analyze and explain any mathematical, scientific, or educational content visible in this image."""
                
                return {
                    "type": "image",
                    "content": description,
                    "base64": img_base64,
                    "metadata": {
                        "width": width,
                        "height": height,
                        "format": format_type,
                        "mode": mode
                    }
                }
                
        except Exception as e:
            return {"type": "error", "content": f"Error processing image: {str(e)}"}
    
    def process_document(self, doc_path: str) -> Dict[str, str]:
        """Process PDF, DOCX, and text documents"""
        file_ext = os.path.splitext(doc_path)[1].lower()
        
        try:
            if file_ext == '.pdf':
                return self.process_pdf(doc_path)
            elif file_ext in ['.docx', '.doc']:
                return self.process_docx(doc_path)
            elif file_ext == '.txt':
                return self.process_text(doc_path)
            else:
                return {"type": "error", "content": "Unsupported document format"}
                
        except Exception as e:
            return {"type": "error", "content": f"Error processing document: {str(e)}"}
    
    def process_pdf(self, pdf_path: str) -> Dict[str, str]:
        """Extract text from PDF files"""
        try:
            with open(pdf_path, 'rb') as file:
                pdf_reader = PyPDF2.PdfReader(file)
                text_content = ""
                
                # Extract text from all pages (limit to first 10 for performance)
                max_pages = min(10, len(pdf_reader.pages))
                for page_num in range(max_pages):
                    page = pdf_reader.pages[page_num]
                    text_content += page.extract_text() + "\n\n"
                
                # Truncate if too long
                if len(text_content) > 5000:
                    text_content = text_content[:5000] + "\n\n[Content truncated for processing...]"
                
                analysis = f"""PDF DOCUMENT ANALYSIS:
- Total Pages: {len(pdf_reader.pages)}
- Pages Processed: {max_pages}
- Extracted Text Length: {len(text_content)} characters

EXTRACTED CONTENT:
{text_content}

EDUCATIONAL CONTEXT:
I can help you with any questions about this document, including:
- Explaining concepts mentioned in the text
- Solving problems presented
- Summarizing key points
- Analyzing educational content"""
                
                return {
                    "type": "pdf",
                    "content": analysis,
                    "extracted_text": text_content,
                    "metadata": {
                        "total_pages": len(pdf_reader.pages),
                        "processed_pages": max_pages
                    }
                }
                
        except Exception as e:
            return {"type": "error", "content": f"Error processing PDF: {str(e)}"}
    
    def process_docx(self, docx_path: str) -> Dict[str, str]:
        """Extract text from DOCX files"""
        try:
            doc = docx.Document(docx_path)
            text_content = ""
            
            # Extract text from all paragraphs
            for paragraph in doc.paragraphs:
                text_content += paragraph.text + "\n"
            
            # Truncate if too long
            if len(text_content) > 5000:
                text_content = text_content[:5000] + "\n\n[Content truncated for processing...]"
            
            analysis = f"""WORD DOCUMENT ANALYSIS:
- Paragraphs: {len(doc.paragraphs)}
- Extracted Text Length: {len(text_content)} characters

EXTRACTED CONTENT:
{text_content}

EDUCATIONAL CONTEXT:
I can help you with any educational content in this document, including:
- Explaining concepts and topics
- Answering questions about the material
- Providing additional context and examples
- Helping with assignments or homework"""
            
            return {
                "type": "docx",
                "content": analysis,
                "extracted_text": text_content,
                "metadata": {
                    "paragraphs": len(doc.paragraphs)
                }
            }
            
        except Exception as e:
            return {"type": "error", "content": f"Error processing DOCX: {str(e)}"}
    
    def process_text(self, txt_path: str) -> Dict[str, str]:
        """Process plain text files"""
        try:
            with open(txt_path, 'r', encoding='utf-8') as file:
                text_content = file.read()
            
            # Truncate if too long
            if len(text_content) > 5000:
                text_content = text_content[:5000] + "\n\n[Content truncated for processing...]"
            
            analysis = f"""TEXT FILE ANALYSIS:
- File Size: {os.path.getsize(txt_path)} bytes
- Character Count: {len(text_content)}
- Line Count: {text_content.count(chr(10)) + 1}

CONTENT:
{text_content}

EDUCATIONAL CONTEXT:
I can help you with any educational content in this text file."""
            
            return {
                "type": "text",
                "content": analysis,
                "extracted_text": text_content
            }
            
        except Exception as e:
            return {"type": "error", "content": f"Error processing text file: {str(e)}"}
    
    def process_data_file(self, data_path: str) -> Dict[str, str]:
        """Process CSV and Excel files"""
        file_ext = os.path.splitext(data_path)[1].lower()
        
        try:
            if file_ext == '.csv':
                df = pd.read_csv(data_path)
            elif file_ext in ['.xlsx', '.xls']:
                df = pd.read_excel(data_path)
            else:
                return {"type": "error", "content": "Unsupported data format"}
            
            # Basic analysis
            rows, cols = df.shape
            columns = list(df.columns)
            
            # Sample data (first 5 rows)
            sample_data = df.head().to_string()
            
            # Basic statistics for numeric columns
            numeric_summary = ""
            numeric_cols = df.select_dtypes(include=['number']).columns
            if len(numeric_cols) > 0:
                numeric_summary = f"\nNUMERIC COLUMN STATISTICS:\n{df[numeric_cols].describe().to_string()}"
            
            analysis = f"""DATA FILE ANALYSIS:
- Format: {file_ext.upper()}
- Dimensions: {rows} rows × {cols} columns
- Columns: {', '.join(columns[:10])}{'...' if len(columns) > 10 else ''}

SAMPLE DATA (First 5 rows):
{sample_data}

{numeric_summary}

EDUCATIONAL CONTEXT:
I can help you with:
- Data analysis and interpretation
- Statistical calculations
- Creating visualizations (descriptions)
- Understanding data patterns and trends
- Homework involving data science"""
            
            return {
                "type": "data",
                "content": analysis,
                "dataframe": df,
                "metadata": {
                    "rows": rows,
                    "columns": cols,
                    "column_names": columns
                }
            }
            
        except Exception as e:
            return {"type": "error", "content": f"Error processing data file: {str(e)}"}
    
    def process_code_file(self, code_path: str) -> Dict[str, str]:
        """Process code files"""
        file_ext = os.path.splitext(code_path)[1].lower()
        
        try:
            with open(code_path, 'r', encoding='utf-8') as file:
                code_content = file.read()
            
            # Truncate if too long
            if len(code_content) > 3000:
                code_content = code_content[:3000] + "\n\n[Code truncated for processing...]"
            
            # Count lines
            line_count = code_content.count('\n') + 1
            
            analysis = f"""CODE FILE ANALYSIS:
- Language: {file_ext[1:].upper()}
- Lines of Code: {line_count}
- File Size: {os.path.getsize(code_path)} bytes

CODE CONTENT:
```{file_ext[1:]}
{code_content}
```

EDUCATIONAL CONTEXT:
I can help you with:
- Code explanation and analysis
- Debugging and optimization suggestions
- Algorithm explanations
- Programming concept clarification
- Homework and project assistance"""
            
            return {
                "type": "code",
                "content": analysis,
                "code": code_content,
                "language": file_ext[1:],
                "metadata": {
                    "lines": line_count,
                    "language": file_ext[1:]
                }
            }
            
        except Exception as e:
            return {"type": "error", "content": f"Error processing code file: {str(e)}"}

class MultimodalDatasetSupremacyAI:
    """Enhanced Dataset Supremacy AI with multimodal capabilities"""
    
    def __init__(self):
        # Initialize base dataset system
        from __main__ import DatasetPoweredRouter
        self.router = DatasetPoweredRouter()
        self.groq_url = "https://api.groq.com/openai/v1/chat/completions"
        
        # Add multimodal processor
        self.multimodal = MultimodalProcessor()
        
        # Dataset collections (same as before)
        self.datasets = {}
        self.examples = {}
        self.dataset_metadata = {}
        self.loading_status = "🚀 Loading Multimodal Dataset Supremacy AI..."
        self.total_examples = 0
        
        # Enhanced analytics
        self.stats = {
            "total_queries": 0,
            "file_uploads": 0,
            "file_types": {},
            "dataset_usage": {},
            "model_usage": {},
            "subjects": {},
            "response_times": [],
            "multimodal_queries": 0
        }
        
        # Load datasets (reuse existing logic)
        self.load_dataset_supremacy()
    
    def load_dataset_supremacy(self):
        """Load comprehensive educational datasets (same logic as before)"""
        def load_thread():
            try:
                if not DATASETS_AVAILABLE:
                    self.loading_status = "✅ Multimodal AI Ready (Premium fallback mode)"
                    self.create_premium_dataset_fallbacks()
                    return
                
                self.loading_status = "🔥 Loading Multimodal Dataset Collection..."
                
                # Core datasets (simplified for demo)
                core_datasets = [
                    ("lighteval/MATH", "competition_math", 1500),
                    ("meta-math/MetaMathQA", "math_reasoning", 2000),
                    ("gsm8k", "basic_math", 2000),
                    ("allenai/ai2_arc", "science_reasoning", 1500),
                    ("sciq", "science_qa", 1500),
                    ("sahil2801/CodeAlpaca-20k", "basic_coding", 1500),
                    ("cais/mmlu", "university_knowledge", 1500),
                    ("yahma/alpaca-cleaned", "general_education", 2000)
                ]
                
                loaded_count = 0
                for dataset_name, category, sample_size in core_datasets:
                    try:
                        self.loading_status = f"📚 Loading {dataset_name}..."
                        
                        if "mmlu" in dataset_name:
                            dataset = load_dataset(dataset_name, "all", split=f"train[:{sample_size}]")
                        else:
                            dataset = load_dataset(dataset_name, split=f"train[:{sample_size}]")
                        
                        processed_examples = self.process_dataset(dataset, category, dataset_name)
                        if processed_examples:
                            self.datasets[category] = dataset
                            self.examples[category] = processed_examples
                            self.dataset_metadata[category] = {
                                "source": dataset_name,
                                "size": len(processed_examples),
                                "quality": 9
                            }
                            loaded_count += 1
                            print(f"✅ {dataset_name} → {len(processed_examples)} examples")
                        
                    except Exception as e:
                        print(f"⚠️ {dataset_name} unavailable: {e}")
                        continue
                
                self.total_examples = sum(len(examples) for examples in self.examples.values())
                
                if self.total_examples > 0:
                    self.loading_status = f"✅ MULTIMODAL AI READY - {loaded_count} datasets, {self.total_examples:,} examples"
                else:
                    self.loading_status = "✅ Multimodal AI Ready (Core functionality active)"
                    self.create_premium_dataset_fallbacks()
                
                print(f"🎓 Multimodal Dataset Supremacy AI ready with {self.total_examples:,} examples")
                
            except Exception as e:
                self.loading_status = "✅ Multimodal AI Ready (Fallback mode)"
                self.create_premium_dataset_fallbacks()
                print(f"Dataset loading info: {e}")
        
        thread = threading.Thread(target=load_thread)
        thread.daemon = True
        thread.start()
    
    def process_dataset(self, dataset, category, source_name):
        """Process datasets (simplified version)"""
        examples = []
        
        for item in dataset:
            try:
                processed = None
                
                if category == "competition_math" and item.get('problem') and item.get('solution'):
                    processed = {
                        'question': item['problem'],
                        'solution': item['solution'],
                        'type': 'competition',
                        'subject': 'mathematics',
                        'quality': 10
                    }
                elif category in ["math_reasoning", "basic_math"] and item.get('question') and item.get('answer'):
                    processed = {
                        'question': item['question'],
                        'solution': item['answer'],
                        'type': 'math_problem',
                        'subject': 'mathematics',
                        'quality': 9
                    }
                elif category in ["science_reasoning", "science_qa"]:
                    if item.get('question') and item.get('correct_answer'):
                        processed = {
                            'question': item['question'],
                            'solution': item['correct_answer'],
                            'type': 'science',
                            'subject': 'science',
                            'quality': 8
                        }
                
                if processed and len(processed['question']) > 20:
                    examples.append(processed)
                    
            except Exception:
                continue
        
        return examples[:150]  # Keep top 150 per category
    
    def create_premium_dataset_fallbacks(self):
        """Create fallback examples"""
        self.examples = {
            'competition_math': [{
                'question': 'Prove that √2 is irrational',
                'solution': 'Assume √2 is rational, so √2 = p/q where p,q are integers with gcd(p,q)=1...',
                'type': 'proof',
                'subject': 'mathematics',
                'quality': 10
            }],
            'basic_math': [{
                'question': 'Solve x² - 5x + 6 = 0',
                'solution': 'Factor: (x-2)(x-3) = 0, so x = 2 or x = 3',
                'type': 'algebra',
                'subject': 'mathematics',
                'quality': 9
            }]
        }
        self.total_examples = 10
    
    async def educate_multimodal_async(self, question, files=None, subject="general", 
                                     difficulty="intermediate", language="English"):
        """Enhanced education function with multimodal support"""
        
        # Analytics tracking
        self.stats["total_queries"] += 1
        self.stats["subjects"][subject] = self.stats["subjects"].get(subject, 0) + 1
        start_time = time.time()
        
        # Process uploaded files
        file_context = ""
        if files and len(files) > 0:
            self.stats["file_uploads"] += 1
            self.stats["multimodal_queries"] += 1
            
            file_analyses = []
            for file_path in files:
                if file_path:  # Check if file exists
                    file_result = self.multimodal.process_file(file_path)
                    file_analyses.append(file_result)
                    
                    # Track file types
                    file_type = file_result.get("type", "unknown")
                    self.stats["file_types"][file_type] = self.stats["file_types"].get(file_type, 0) + 1
            
            # Build file context for prompt
            if file_analyses:
                file_context = "\n\nFILE ANALYSIS:\n"
                for i, analysis in enumerate(file_analyses, 1):
                    file_context += f"\nFile {i}:\n{analysis['content']}\n"
                file_context += "\nPlease consider the uploaded file(s) when answering the question.\n"
        
        if not question.strip() and not file_context:
            return "🎓 Welcome to Multimodal Dataset Supremacy AI! Ask questions and upload files (images, PDFs, documents, data) for enhanced educational assistance!"
        
        # Enhanced query analysis considering file context
        query_type = self.router.analyze_query_complexity(question, subject, difficulty)
        if file_context and ("image" in file_context.lower() or "pdf" in file_context.lower()):
            # Boost complexity for multimodal queries
            if query_type == "quick_facts":
                query_type = "general"
        
        routing_config = self.router.dataset_routing[query_type]
        selected_model = routing_config["model"]
        
        # Track usage
        self.stats["model_usage"][selected_model] = self.stats["model_usage"].get(selected_model, 0) + 1
        self.stats["dataset_usage"][query_type] = self.stats["dataset_usage"].get(query_type, 0) + 1
        
        # Get relevant examples from datasets
        examples = self.get_optimal_examples(question, query_type, routing_config["examples"])
        
        # Create enhanced prompt with file context and datasets
        system_prompt = f"""You are a multimodal educational AI enhanced with premium datasets and file processing capabilities.

DATASET ENHANCEMENT:
You have access to premium educational datasets including competition mathematics, advanced science reasoning, programming excellence, and academic knowledge.
"""
        
        if examples:
            system_prompt += "\n\nPREMIUM DATASET EXAMPLES:\n"
            for i, ex in enumerate(examples, 1):
                system_prompt += f"\nExample {i}:\nQ: {ex['question'][:200]}...\nA: {ex['solution'][:200]}...\n"
        
        system_prompt += f"""
MULTIMODAL CAPABILITIES:
- I can analyze images, PDFs, documents, spreadsheets, and code files
- I provide educational context for all uploaded materials
- I combine file analysis with dataset-enhanced responses

{file_context}

TASK: Provide a comprehensive educational response that:
- Uses dataset-quality explanations and examples
- Incorporates analysis of any uploaded files
- Shows step-by-step reasoning when appropriate
- Provides educational context and applications
- Subject: {subject} | Difficulty: {difficulty}
"""
        
        if language != "English":
            system_prompt += f"\n\nIMPORTANT: Respond in {language}."
        
        # Prepare messages
        user_message = question if question.strip() else "Please analyze and explain the uploaded file(s) from an educational perspective."
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_message}
        ]
        
        try:
            # Call Groq model
            response = await self.call_groq_model(selected_model, messages, routing_config["temperature"])
            
            response_time = time.time() - start_time
            self.stats["response_times"].append(response_time)
            
            if response:
                # Enhanced footer with multimodal info
                model_name = self.router.models[selected_model]["name"]
                file_info = f" • {len(files)} file(s)" if files and len(files) > 0 else ""
                
                footer = f"\n\n---\n*🎓 **{model_name}** enhanced with premium datasets{file_info} • {self.total_examples:,} examples • {response_time:.2f}s • Multimodal Query #{self.stats['multimodal_queries']:,}*"
                
                return response + footer
            else:
                return "⚠️ Service temporarily unavailable. Please try again."
                
        except Exception as e:
            return f"🔧 Technical issue. Please try again."
    
    def get_optimal_examples(self, question, query_type, num_examples=2):
        """Get relevant examples from datasets"""
        routing_config = self.router.dataset_routing.get(query_type, self.router.dataset_routing["general"])
        target_datasets = routing_config["datasets"]
        
        all_examples = []
        for dataset_category in target_datasets:
            if dataset_category in self.examples:
                all_examples.extend(self.examples[dataset_category])
        
        if not all_examples:
            for category_examples in self.examples.values():
                all_examples.extend(category_examples)
        
        if all_examples:
            return random.sample(all_examples, min(num_examples, len(all_examples)))
        return []
    
    async def call_groq_model(self, model_id, messages, temperature=0.2):
        """Call Groq model"""
        model_config = self.router.models[model_id]
        
        headers = {
            "Authorization": f"Bearer {self.router.groq_api_key}",
            "Content-Type": "application/json"
        }
        
        payload = {
            "model": model_config["model_id"],
            "messages": messages,
            "temperature": temperature,
            "max_tokens": model_config["max_tokens"]
        }
        
        async with aiohttp.ClientSession() as session:
            async with session.post(self.groq_url, headers=headers, json=payload, timeout=25) as response:
                if response.status == 200:
                    result = await response.json()
                    return result["choices"][0]["message"]["content"]
                else:
                    raise Exception(f"Groq API error: {response.status}")
    
    def educate_multimodal(self, question, files=None, subject="general", difficulty="intermediate", language="English"):
        """Synchronous wrapper"""
        try:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            return loop.run_until_complete(
                self.educate_multimodal_async(question, files, subject, difficulty, language)
            )
        except Exception as e:
            return f"🔧 System error. Please try again."
        finally:
            loop.close()
    
    def get_multimodal_analytics(self):
        """Get comprehensive analytics including multimodal stats"""
        total = self.stats["total_queries"]
        multimodal_percent = (self.stats["multimodal_queries"] / total * 100) if total > 0 else 0
        
        file_stats = ""
        for file_type, count in sorted(self.stats["file_types"].items(), key=lambda x: x[1], reverse=True):
            file_stats += f"\n• {file_type.title()}: {count} files"
        
        analytics = f"""📊 **MULTIMODAL DATASET SUPREMACY ANALYTICS**

🚀 **Performance:**
• Total Queries: {total:,}
• Multimodal Queries: {self.stats['multimodal_queries']:,} ({multimodal_percent:.1f}%)
• File Uploads: {self.stats['file_uploads']:,}
• Dataset Examples: {self.total_examples:,}

📁 **File Processing:**{file_stats if file_stats else "\n• No files processed yet"}

🤖 **Model Usage:**"""
        
        for model, count in sorted(self.stats["model_usage"].items(), key=lambda x: x[1], reverse=True):
            model_name = self.router.models[model]["name"]
            percentage = (count / total * 100) if total > 0 else 0
            analytics += f"\n• {model_name}: {count} ({percentage:.1f}%)"
        
        analytics += f"""

📚 **Supported Formats:**
• Images: PNG, JPG, GIF, BMP, WebP
• Documents: PDF, DOCX, TXT
• Data: CSV, Excel (XLSX, XLS)
• Code: Python, JavaScript, Java, C++, HTML

🌟 **Status:** {self.loading_status}"""
        
        return analytics

# Initialize Multimodal Dataset Supremacy AI
multimodal_ai = MultimodalDatasetSupremacyAI()

def create_multimodal_interface():
    """Create the ultimate multimodal education interface"""
    
    with gr.Blocks(
        theme=gr.themes.Origin(),
        title="🌍 Multimodal Dataset Supremacy AI - Images + PDFs + Premium Datasets",
        css="""
        .header {
            text-align: center;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%);
            padding: 3rem;
            border-radius: 20px;
            margin-bottom: 2rem;
            box-shadow: 0 15px 35px rgba(0,0,0,0.1);
        }
        .multimodal-power {
            background: linear-gradient(135deg, #ffecd2 0%, #fcb69f 100%);
            border-radius: 15px;
            padding: 1.5rem;
            margin: 1rem 0;
        }
        """
    ) as demo:
        
        # Multimodal Header
        gr.HTML("""
        <div class="header">
            <h1 style="color: white; margin: 0; font-size: 3.5em; font-weight: 800;">🌍 MULTIMODAL DATASET SUPREMACY AI</h1>
            <p style="color: #f0f0f0; margin: 1rem 0 0 0; font-size: 1.4em; font-weight: 400;">
                Images + PDFs + Documents + Premium Datasets = Ultimate Educational AI
            </p>
            <div style="margin-top: 1.5rem;">
                <span style="background: rgba(255,255,255,0.25); padding: 0.7rem 1.2rem; border-radius: 25px; margin: 0.3rem; display: inline-block; color: white; font-weight: 600;">📱 Images</span>
                <span style="background: rgba(255,255,255,0.25); padding: 0.7rem 1.2rem; border-radius: 25px; margin: 0.3rem; display: inline-block; color: white; font-weight: 600;">📄 PDFs</span>
                <span style="background: rgba(255,255,255,0.25); padding: 0.7rem 1.2rem; border-radius: 25px; margin: 0.3rem; display: inline-block; color: white; font-weight: 600;">💻 Code</span>
                <span style="background: rgba(255,255,255,0.25); padding: 0.7rem 1.2rem; border-radius: 25px; margin: 0.3rem; display: inline-block; color: white; font-weight: 600;">📚 Datasets</span>
            </div>
        </div>
        """)
        
        # Main Interface
        with gr.Row():
            with gr.Column(scale=3):
                with gr.Group():
                    # File Upload Section
                    gr.HTML('<h3 style="margin-bottom: 1rem;">📁 Upload Files (Optional)</h3>')
                    file_upload = gr.Files(
                        label="Upload Images, PDFs, Documents, Data Files, or Code",
                        file_types=[
                            ".png", ".jpg", ".jpeg", ".gif", ".bmp", ".webp",  # Images
                            ".pdf", ".docx", ".doc", ".txt",  # Documents  
                            ".csv", ".xlsx", ".xls",  # Data
                            ".py", ".js", ".html", ".css", ".java", ".cpp", ".c"  # Code
                        ],
                        file_count="multiple"
                    )
                    
                    # Question Input
                    question_input = gr.Textbox(
                        label="🎓 Your Educational Question",
                        placeholder="Ask about uploaded files OR any educational topic. I'll enhance responses with premium datasets!",
                        lines=4,
                        max_lines=10
                    )
                    
                    with gr.Row():
                        subject_dropdown = gr.Dropdown(
                            choices=[
                                "general", "mathematics", "science", "physics", "chemistry", 
                                "biology", "computer_science", "programming", "english", 
                                "literature", "history", "philosophy", "economics", 
                                "engineering", "medicine", "psychology", "data_science"
                            ],
                            label="📚 Subject",
                            value="general",
                            interactive=True
                        )
                        
                        difficulty_dropdown = gr.Dropdown(
                            choices=["beginner", "intermediate", "advanced", "competition", "graduate", "phd"],
                            label="⚡ Level",
                            value="intermediate",
                            interactive=True
                        )
                        
                        language_dropdown = gr.Dropdown(
                            choices=["English", "Spanish", "French", "German", "Chinese", "Japanese", "Portuguese", "Italian"],
                            label="🌐 Language",
                            value="English",
                            interactive=True
                        )
                    
                    submit_btn = gr.Button(
                        "🚀 Get Multimodal Answer", 
                        variant="primary", 
                        size="lg"
                    )
            
            with gr.Column(scale=1):
                with gr.Group():
                    gr.HTML('<div class="multimodal-power"><h3>🌍 Multimodal Power Status</h3></div>')
                    
                    analytics_display = gr.Textbox(
                        label="📊 Multimodal Analytics",
                        value=multimodal_ai.get_multimodal_analytics(),
                        lines=20,
                        interactive=False
                    )
                    
                    refresh_btn = gr.Button("🔄 Refresh Analytics", size="sm")
        
        # Response Area
        answer_output = gr.Textbox(
            label="📖 Multimodal Dataset-Enhanced Response",
            lines=22,
            max_lines=35,
            interactive=False,
            placeholder="Your premium, multimodal, dataset-enhanced educational response will appear here..."
        )
        
        # Multimodal Examples Section
        with gr.Group():
            gr.HTML('<h3 style="text-align: center; margin: 1rem 0;">🌟 Multimodal Dataset Supremacy Examples</h3>')
            
            # Text-only examples (dataset-powered)
            with gr.Accordion("📚 Dataset-Enhanced Examples (No Files)", open=False):
                gr.Examples(
                    examples=[
                        # Competition Mathematics
                        ["Prove that there are infinitely many prime numbers using Euclid's method", None, "mathematics", "competition", "English"],
                        ["Solve the differential equation dy/dx = xy with initial condition y(0) = 1", None, "mathematics", "advanced", "English"],
                        
                        # Advanced Sciences  
                        ["Explain the double-slit experiment and its implications for quantum mechanics", None, "physics", "advanced", "English"],
                        ["Describe the mechanism of enzyme catalysis using the induced fit model", None, "biology", "advanced", "English"],
                        
                        # Programming
                        ["Implement a binary search algorithm and analyze its time complexity", None, "programming", "intermediate", "English"],
                        ["Explain object-oriented programming principles with examples", None, "computer_science", "intermediate", "English"],
                    ],
                    inputs=[question_input, file_upload, subject_dropdown, difficulty_dropdown, language_dropdown],
                    outputs=answer_output,
                    fn=multimodal_ai.educate_multimodal,
                    cache_examples=False
                )
            
            # Multimodal examples (with file instructions)
            with gr.Accordion("📁 Multimodal Examples (Upload Files)", open=True):
                gr.HTML("""
                <div style="padding: 1rem; background: #f8f9fa; border-radius: 10px; margin: 1rem 0;">
                    <h4>🎯 Try These Multimodal Scenarios:</h4>
                    <ul style="margin: 0.5rem 0;">
                        <li><strong>📷 Math Problems:</strong> Upload image of handwritten equation → Ask "Solve this step by step"</li>
                        <li><strong>📄 PDF Analysis:</strong> Upload textbook PDF → Ask "Explain the key concepts in this chapter"</li>
                        <li><strong>📊 Data Science:</strong> Upload CSV file → Ask "Analyze this data and find patterns"</li>
                        <li><strong>💻 Code Review:</strong> Upload Python file → Ask "Explain this code and suggest improvements"</li>
                        <li><strong>📋 Document Help:</strong> Upload assignment PDF → Ask "Help me understand these problems"</li>
                        <li><strong>🖼️ Diagrams:</strong> Upload scientific diagram → Ask "Explain what this illustration shows"</li>
                    </ul>
                    <p style="margin: 0.5rem 0; font-style: italic;">Mix file uploads with dataset-enhanced explanations for ultimate educational power!</p>
                </div>
                """)
        
        # Event Handlers
        submit_btn.click(
            fn=multimodal_ai.educate_multimodal,
            inputs=[question_input, file_upload, subject_dropdown, difficulty_dropdown, language_dropdown],
            outputs=answer_output,
            api_name="predict"
        )
        
        question_input.submit(
            fn=multimodal_ai.educate_multimodal,
            inputs=[question_input, file_upload, subject_dropdown, difficulty_dropdown, language_dropdown],
            outputs=answer_output
        )
        
        refresh_btn.click(
            fn=multimodal_ai.get_multimodal_analytics,
            outputs=analytics_display
        )
        
        # Comprehensive Footer
        gr.HTML("""
        <div style="text-align: center; margin-top: 3rem; padding: 2rem; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 20px; color: white;">
            <h3 style="margin-bottom: 1rem; font-size: 1.8em;">🌍 Ultimate Educational AI Architecture</h3>
            
            <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 2rem; margin: 1.5rem 0;">
                <div style="background: rgba(255,255,255,0.1); padding: 1.5rem; border-radius: 10px;">
                    <h4 style="margin-bottom: 1rem;">📁 Multimodal Capabilities</h4>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Images:</strong> PNG, JPG, GIF, BMP, WebP analysis</p>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Documents:</strong> PDF text extraction, DOCX processing</p>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Data Files:</strong> CSV, Excel analysis & statistics</p>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Code Files:</strong> Python, JS, Java, C++ explanation</p>
                </div>
                
                <div style="background: rgba(255,255,255,0.1); padding: 1.5rem; border-radius: 10px;">
                    <h4 style="margin-bottom: 1rem;">📚 Dataset Supremacy</h4>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Competition Math:</strong> AMC, AIME, USAMO problems</p>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Science Reasoning:</strong> University-level science QA</p>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Programming:</strong> Industry-standard code examples</p>
                    <p style="margin: 0.5rem 0; font-size: 0.9em;"><strong>Academic Knowledge:</strong> Research-quality content</p>
                </div>
            </div>
            
            <div style="margin: 1.5rem 0; padding: 1rem; background: rgba(255,255,255,0.15); border-radius: 10px;">
                <h4 style="margin-bottom: 1rem;">🎯 Competitive Advantages</h4>
                <div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 1rem; text-align: left;">
                    <div>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ 100% Free Operation</p>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ File Processing</p>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ Premium Datasets</p>
                    </div>
                    <div>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ Smart Model Routing</p>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ Multi-language Support</p>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ K-PhD Coverage</p>
                    </div>
                    <div>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ Ultra-fast Groq Speed</p>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ Educational Focus</p>
                        <p style="margin: 0.3rem 0; font-size: 0.9em;">✅ Scalable Architecture</p>
                    </div>
                </div>
            </div>
            
            <div style="margin-top: 1.5rem; padding: 1rem; background: rgba(255,255,255,0.1); border-radius: 10px;">
                <p style="margin: 0; font-size: 0.9em;">
                    🚀 <strong>API Endpoint:</strong> https://memoroeisdead-your-education-api.hf.space/run/predict<br>
                    💡 <strong>Mission:</strong> Prove that premium datasets + file processing beats expensive models<br>
                    🎯 <strong>Result:</strong> The most advanced, cost-effective educational AI in existence
                </p>
            </div>
        </div>
        """)
    
    return demo

if __name__ == "__main__":
    interface = create_multimodal_interface()
    interface.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        show_tips=True,
        enable_queue=True,
        max_threads=50
    )